Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "55" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 38 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 36 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459853 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.386904 | 16.636244 | -0.221165 | 26.918892 | 6.659146 | 10.891833 | 4.040445 | 1.935329 | 0.7499 | 0.0352 | 0.5483 | 3.129552 | 1.135533 |
| 2459852 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.503541 | 16.894284 | 0.086756 | 28.186270 | 7.496430 | 19.866846 | 0.043526 | 16.413323 | 0.8469 | 0.0319 | 0.5578 | 4.136400 | 1.134040 |
| 2459851 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.630591 | 21.853997 | -0.769818 | 30.120887 | 1.429200 | 43.449230 | 0.421892 | 19.598780 | 0.7770 | 0.0551 | 0.5612 | 4.287659 | 1.151795 |
| 2459850 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.694830 | 3.479261 | 0.056654 | 0.564463 | 6.569703 | 0.010035 | 1.067303 | -0.498570 | 0.7559 | 0.7595 | 0.3279 | 2.956047 | 2.435250 |
| 2459849 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.877400 | 3.924277 | 3.564063 | 1.113874 | 6.982426 | -0.389912 | 2.601044 | -0.027272 | 0.7529 | 0.7538 | 0.3335 | 4.078063 | 3.054654 |
| 2459848 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.215527 | 3.961324 | -0.282555 | -0.376686 | 11.245017 | 0.249683 | -0.055625 | -0.349392 | 0.7343 | 0.7539 | 0.3549 | 3.556990 | 2.812484 |
| 2459847 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.244633 | 3.919662 | 0.023378 | -0.142573 | 4.802614 | -0.815830 | 0.645463 | -0.668343 | 0.7377 | 0.6912 | 0.4063 | 3.319354 | 2.742363 |
| 2459846 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 33.33% | 66.67% | 1.205725 | 2.174907 | -0.660864 | 0.080104 | 1.819713 | 1.962314 | 1.849244 | -0.354257 | 0.8541 | 0.7020 | 0.4533 | 3.483374 | 2.857191 |
| 2459845 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.972048 | 5.118086 | 1.311674 | -0.417707 | 8.539411 | -0.539460 | 0.527739 | -1.169679 | 0.7397 | 0.7551 | 0.3534 | 5.844570 | 7.142165 |
| 2459844 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.743149 | -0.154859 | 0.458716 | 0.231418 | 2.016976 | -0.138594 | 1.054761 | 0.584729 | 0.0267 | 0.0245 | 0.0015 | nan | nan |
| 2459843 | digital_ok | 100.00% | 0.66% | 0.66% | 0.00% | 100.00% | 0.00% | 4.213594 | 4.709007 | 0.267848 | -0.344216 | 3.614181 | -0.411944 | 0.386832 | -0.588137 | 0.7491 | 0.7558 | 0.3670 | 4.380011 | 4.047868 |
| 2459840 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.939998 | 0.358478 | 0.238752 | -0.033514 | 1.180098 | 0.299694 | 0.619582 | 1.249238 | 0.0261 | 0.0231 | 0.0017 | nan | nan |
| 2459839 | digital_ok | 0.00% | - | - | - | - | - | -1.153765 | -0.865893 | 0.605645 | 0.427376 | -0.867569 | -1.253978 | 0.899576 | 1.528703 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 88.462079 | 86.952183 | 79.594887 | 67.646916 | 104.530137 | 91.918514 | 1016.687135 | 605.566286 | 0.0176 | 0.0166 | 0.0008 | 1.041720 | 1.039422 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0404 | 0.0394 | 0.0026 | nan | nan |
| 2459835 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.092047 | 0.593786 | 1.222709 | 0.230043 | 1.416150 | -0.519946 | 0.355104 | -0.705381 | 0.0390 | 0.0399 | 0.0023 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 1.023511 | 1.421945 | -0.605571 | -0.740376 | 10.907655 | -0.604741 | 1.109594 | 0.684455 | 0.0366 | 0.0576 | 0.0013 | nan | nan |
| 2459832 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 3.118907 | 2.261657 | 0.539637 | 0.112830 | 9.350214 | -0.994020 | 6.729819 | -0.559940 | 0.0701 | 0.0681 | 0.0077 | 1.190970 | 1.193225 |
| 2459831 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.511329 | 1.461632 | 0.368192 | 0.343624 | -1.282820 | -1.682292 | 0.500410 | 0.853583 | 0.0474 | 0.0632 | 0.0021 | nan | nan |
| 2459830 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 3.927331 | 1.633326 | -0.169192 | 0.595718 | 1.389618 | -0.830963 | 7.579902 | -0.304383 | 0.0673 | 0.0643 | 0.0082 | 1.227429 | 1.228226 |
| 2459829 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 3.953196 | 5.354521 | 0.133820 | 0.680861 | 8.985363 | -0.970833 | 7.341387 | 0.164093 | 0.0689 | 0.0657 | 0.0065 | 71.642859 | 38.446961 |
| 2459828 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 3.233297 | 1.412588 | 0.605343 | 0.417904 | 0.838430 | -0.522142 | 10.066535 | 1.019621 | 0.0657 | 0.0612 | 0.0063 | 1.275098 | 1.268664 |
| 2459827 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.591775 | 3.671415 | -0.545279 | 1.633052 | 4.909692 | -0.710820 | 6.685251 | 0.629137 | 0.0648 | 0.0673 | 0.0063 | 1.227293 | 1.225536 |
| 2459826 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.896971 | 0.741068 | -0.196837 | 1.452309 | 2.144480 | -1.096515 | 10.053465 | -0.301504 | 0.0606 | 0.0563 | 0.0051 | 0.000000 | 0.000000 |
| 2459825 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 3.527169 | 0.668160 | -0.020743 | 0.323262 | 0.511069 | -0.930102 | 0.894708 | -0.888181 | 0.0687 | 0.0677 | 0.0075 | 1.210880 | 1.213590 |
| 2459824 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.563886 | 4.134846 | 0.730822 | 0.733432 | 4.928707 | -0.235789 | 2.135677 | -0.381617 | 0.0649 | 0.0764 | 0.0063 | 1.225752 | 1.224122 |
| 2459823 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 3.644528 | 0.331745 | 0.935400 | 1.269704 | 1.047472 | -0.906996 | 17.276892 | 0.558736 | 0.0678 | 0.0672 | 0.0075 | 1.196664 | 1.196224 |
| 2459822 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 4.570805 | 0.801800 | -0.513871 | 0.972569 | 2.592200 | -1.037868 | 5.578910 | -0.621351 | 0.0792 | 0.0673 | 0.0065 | 1.164903 | 1.166528 |
| 2459821 | digital_ok | 100.00% | 11.29% | 11.29% | 0.00% | 100.00% | 0.00% | 4.735157 | 1.429210 | -0.556343 | 1.351625 | 1.962233 | -1.371420 | 1.102954 | -1.478615 | 0.7445 | 0.6134 | 0.4308 | 4.285845 | 3.097760 |
| 2459820 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.770825 | 3.153721 | -0.395571 | 1.223138 | 10.922925 | -1.660857 | 5.955876 | -0.106694 | 0.7859 | 0.7093 | 0.3916 | 4.140242 | 3.651857 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 97.37% | 3.455653 | 0.458394 | -0.591764 | 0.790032 | 0.080776 | -0.059307 | -0.199556 | -0.660758 | 0.8388 | 0.7190 | 0.4851 | 3.058178 | 2.665916 |
| 2459816 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 72.09% | 1.690102 | 0.815812 | 1.022490 | 0.647189 | 0.717153 | 0.558034 | 3.936234 | -0.131956 | 0.8515 | 0.6169 | 0.5666 | 3.899331 | 3.423936 |
| 2459815 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 2.516680 | 0.102842 | 0.815914 | 0.647839 | 0.924670 | 0.256813 | 0.407786 | -0.398321 | 0.8347 | 0.7240 | 0.4993 | 4.549346 | 5.171850 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 6.323092 | 3.234125 | -0.103133 | 0.817911 | 11.733742 | 0.039110 | 8.193745 | 1.172847 | 0.8033 | 0.7477 | 0.3783 | 18.433186 | 29.976508 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | nn Power | 26.918892 | 16.636244 | 0.386904 | 26.918892 | -0.221165 | 10.891833 | 6.659146 | 1.935329 | 4.040445 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | nn Power | 28.186270 | 0.503541 | 16.894284 | 0.086756 | 28.186270 | 7.496430 | 19.866846 | 0.043526 | 16.413323 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | nn Temporal Variability | 43.449230 | 0.630591 | 21.853997 | -0.769818 | 30.120887 | 1.429200 | 43.449230 | 0.421892 | 19.598780 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 6.569703 | 0.694830 | 3.479261 | 0.056654 | 0.564463 | 6.569703 | 0.010035 | 1.067303 | -0.498570 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 6.982426 | 0.877400 | 3.924277 | 3.564063 | 1.113874 | 6.982426 | -0.389912 | 2.601044 | -0.027272 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 11.245017 | 3.961324 | 1.215527 | -0.376686 | -0.282555 | 0.249683 | 11.245017 | -0.349392 | -0.055625 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 4.802614 | 3.919662 | 1.244633 | -0.142573 | 0.023378 | -0.815830 | 4.802614 | -0.668343 | 0.645463 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | nn Shape | 2.174907 | 1.205725 | 2.174907 | -0.660864 | 0.080104 | 1.819713 | 1.962314 | 1.849244 | -0.354257 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 8.539411 | 5.118086 | 1.972048 | -0.417707 | 1.311674 | -0.539460 | 8.539411 | -1.169679 | 0.527739 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 2.016976 | -0.743149 | -0.154859 | 0.458716 | 0.231418 | 2.016976 | -0.138594 | 1.054761 | 0.584729 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | nn Shape | 4.709007 | 4.709007 | 4.213594 | -0.344216 | 0.267848 | -0.411944 | 3.614181 | -0.588137 | 0.386832 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | nn Temporal Discontinuties | 1.249238 | -0.939998 | 0.358478 | 0.238752 | -0.033514 | 1.180098 | 0.299694 | 0.619582 | 1.249238 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | nn Temporal Discontinuties | 1.528703 | -0.865893 | -1.153765 | 0.427376 | 0.605645 | -1.253978 | -0.867569 | 1.528703 | 0.899576 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Discontinuties | 1016.687135 | 86.952183 | 88.462079 | 67.646916 | 79.594887 | 91.918514 | 104.530137 | 605.566286 | 1016.687135 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 1.416150 | 0.593786 | -0.092047 | 0.230043 | 1.222709 | -0.519946 | 1.416150 | -0.705381 | 0.355104 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 10.907655 | 1.421945 | 1.023511 | -0.740376 | -0.605571 | -0.604741 | 10.907655 | 0.684455 | 1.109594 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 9.350214 | 3.118907 | 2.261657 | 0.539637 | 0.112830 | 9.350214 | -0.994020 | 6.729819 | -0.559940 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | nn Shape | 1.461632 | -0.511329 | 1.461632 | 0.368192 | 0.343624 | -1.282820 | -1.682292 | 0.500410 | 0.853583 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Discontinuties | 7.579902 | 3.927331 | 1.633326 | -0.169192 | 0.595718 | 1.389618 | -0.830963 | 7.579902 | -0.304383 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 8.985363 | 5.354521 | 3.953196 | 0.680861 | 0.133820 | -0.970833 | 8.985363 | 0.164093 | 7.341387 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Discontinuties | 10.066535 | 1.412588 | 3.233297 | 0.417904 | 0.605343 | -0.522142 | 0.838430 | 1.019621 | 10.066535 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Discontinuties | 6.685251 | 2.591775 | 3.671415 | -0.545279 | 1.633052 | 4.909692 | -0.710820 | 6.685251 | 0.629137 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Discontinuties | 10.053465 | 0.741068 | 2.896971 | 1.452309 | -0.196837 | -1.096515 | 2.144480 | -0.301504 | 10.053465 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Shape | 3.527169 | 0.668160 | 3.527169 | 0.323262 | -0.020743 | -0.930102 | 0.511069 | -0.888181 | 0.894708 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 4.928707 | 2.563886 | 4.134846 | 0.730822 | 0.733432 | 4.928707 | -0.235789 | 2.135677 | -0.381617 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Discontinuties | 17.276892 | 0.331745 | 3.644528 | 1.269704 | 0.935400 | -0.906996 | 1.047472 | 0.558736 | 17.276892 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Discontinuties | 5.578910 | 4.570805 | 0.801800 | -0.513871 | 0.972569 | 2.592200 | -1.037868 | 5.578910 | -0.621351 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Shape | 4.735157 | 1.429210 | 4.735157 | 1.351625 | -0.556343 | -1.371420 | 1.962233 | -1.478615 | 1.102954 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 10.922925 | 3.770825 | 3.153721 | -0.395571 | 1.223138 | 10.922925 | -1.660857 | 5.955876 | -0.106694 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Shape | 3.455653 | 3.455653 | 0.458394 | -0.591764 | 0.790032 | 0.080776 | -0.059307 | -0.199556 | -0.660758 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Discontinuties | 3.936234 | 0.815812 | 1.690102 | 0.647189 | 1.022490 | 0.558034 | 0.717153 | -0.131956 | 3.936234 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Shape | 2.516680 | 0.102842 | 2.516680 | 0.647839 | 0.815914 | 0.256813 | 0.924670 | -0.398321 | 0.407786 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 55 | N04 | digital_ok | ee Temporal Variability | 11.733742 | 3.234125 | 6.323092 | 0.817911 | -0.103133 | 0.039110 | 11.733742 | 1.172847 | 8.193745 |